RAG systems, a two-node GPU cluster, and a multi-agent platform, all my own builds. It started with a documentation problem at my day job testing rocket engines. I couldn't share that work, so I rebuilt the ideas from scratch on public data.
Status: Open to ML Infrastructure, AI Application, and Forward-Deployed Engineering roles · Remote (US) · Available now
Selected work
The RAG systems are public, with reproducible evals against real corpora. The cluster and agent platform are private infrastructure.
Cited, revision-aware retrieval over 321,124 real IETF RFC chunks, with zero LLM in the answer path. It resolves the real RFC 2616 obsoletion exactly, six ways into RFC 7230 through 7235, by walking a supersession graph.
Routes a question across subject-specialized knowledge modules, binds every claim to a source span, and abstains instead of guessing. Measured head-to-head against a naive RAG baseline on the identical corpus.
A rule-based checker that flags made-up claims in AI-generated text. Tested against a public benchmark, it caught fewer than 3 in 100 fabrications. I put that number at the top of the README instead of burying it, because the failure maps exactly where this whole class of tool stops working.
A modular, offline-capable multi-agent platform (OpenAI-compatible gateway, control plane, autonomy engine, RAG, and IDE integration) with an automated adversarial peer-review step and zero hosted-LLM dependencies.
A self-built two-node multi-GPU cluster serving local models with a privacy-first architecture, thermal-aware autonomy, and GPU-wedge diagnosis and recovery. The RTX 3090 node is down with a hardware fault until I repair it, so the RTX 5090 serves alone for now.
Background
By day nothing ships until it is proven, and I hold my own systems to that same standard: config is the source of truth, defaults fail closed, and every number is labeled measured or estimated instead of asserted.
Toolkit